In recent years, research has been made about object detection to detect various types of objects (such as a person's face and a car) from an image that is captured by a camera or the like. The object detection technology includes learning the features of objects to be detected to create learning data in advance, and comparing the created learning data and image data to determine whether the objects to be detected are included in the image.
With such object detection technology, it is important to reduce the processing load for object detection. There have thus been devised various types of techniques for reducing the processing load. For example, there are known techniques for reducing the processing load by classifying consecutive frames into full search frames where the whole areas of the frames are searched for objects and non-search frames where no search is intended. When an object is found, the vicinity of the object-found position is searched in the subsequent frames (for example, see Japanese Laid-open Patent Publication No. 2004-171490). In an existing quantization technique for image information, image data is subjected to frequency conversion (wavelet transform) and quantization processing is performed based on the magnitudes of the resulting conversion coefficients (or the magnitudes of differences in pixel value between adjoining pixels) (for example, see H. Schneiderman and T. Kanade, “Object Detection Using the Statistics of Parts”, International Journal of Computer Vision, 2002, which is referred to as “Schneiderman” hereinafter).
The foregoing conventional techniques, however, have had the problem that it is not possible to reduce the time necessary for object detection.
For example, when full search frames and non-search frames are set for object detection, the total amount of calculation is reduced in terms of a plurality of frames. With the intermittent, regular appearance of frames to be fully searched for objects, however, the object detection in such full search frames is not shortened at all.